Our contributions.
We introduce an adversarial framework to enforce compositional fairness constraints on graph embeddings for multiple sensitive attributes.
The key idea behind our approach is that we learn a set of adversarial filters that remove information about particular sensitive attributes.
Crucially, each of these learned filters can be optionally applied after training, so the model can flexibly generate embeddings that are invariant with respect to different combinations of sensitive attributes. As the space of possible combinations of sensitive attributes can be combinatorially large, we demonstrate that our compositional strategy can generate invariant embeddings even on unseen combinations at test time. Our contribution is at the intersection of research on (social) graph embedding and algorithmic fairness.
We build upon the success of recent adversarial approaches to fairness [1]}, disentanglement [2]}, and transfer learning [3]}–extending these approaches to the domain of graph representation learning and introducing new algorithmic techniques to accommodate compositional constraints during inference.
